Note that, in baseline system, all the new entities are found by the empty candidate set of name variation process, while the disambiguation component has no contribution.

our approach finds the new entities not only by the empty candidate set, but also leveraging on disambiguation component which also contributes to the performance improvement.


Traditionally, without any training data available, the solution is to rank the candidates based on similarity.

it is difficult for the ranking approach to detect a new entity that is not present in KB, and it is also difficult to combine different features.


A large body of prior research on coreference resolution recasts the problem as a two-class classification problem.

standard supervised machine learning algorithms that minimize classification errors on the training instances do not always lead to maximizing the F-measure of the chosen evaluation metric for coreference resolution.


An SVM-based ranker then picks the output that is likely to have the highest F-measure.

this approach is time-consuming during testing, as F-measure maximization is performed during testing.


Second, unlike the incremental local loss in Daume III (2006), we evaluate the metric score globally.

to Ng (2005), Ng and Cardie (2002a) proposed a rule-induction system with rule pruning.


Ng (2004) varied different components of coreference resolution, choosing the combination of components that results in a classifier with the highest F-measure on a held-out development set during training.

our proposed approach employs instance weighting and beam search to maximize the Fmeasure of the evaluation metric during training.


All our results in this paper were obtained using this reduced feature set and J48 decision tree learning.

given sufficient computational resources, our proposed approach is able to apply to any supervised machine learning algorithms.


The pair is classified wrongly and none of the other pairs in the article can link the two NPs together through clustering.

with MMST, this probability increases to 0.54, which leads to the correct classification.


This paper is partly inspired by their studies.

we do not simply use click information as clues for mining similar queries.


Note that, based on H2 and H3, paraphrase Q-Q and T-T can be directly extracted from raw Q-T pairs.

in consideration of precision, we extract them from paraphrase Q-T. We call our paraphrase Q-Q and T-T extraction approach as a pivot approach, since we use titles as pivots (queries as targets) when extracting paraphrase Q-Q and use queries as pivots (titles as targets) when extracting paraphrase T-T. Our paraphrase extraction algorithm is shown in Table 2.


As expected, the paraphrases we extract cover a variety of domains.

around 50% of them are in the 7 most popular domains 7 , including: (1) health and medicine, (2) documentary download, (3) entertainment, (4) software, (5) ed-ucation and study, (6) computer game, (7) economy and finance.


Milne and Witten's work is most related to what we propose here in that we also employ features similar to their relatedness and commonness features.

we add to this a much richer set of features which are extracted from Web-scale data sources beyond Wikipedia, and we develop a machine learning approach to automatically blend our features using completely automatically generated training data.


There often exist multiple corpora for the same natural language processing (NLP) tasks.

such corpora are generally used independently due to distinctions in annotation standards.


At first sight, a direct combination of multiple corpora is a way to this end.

corpora created for the same NLP tasks are generally built by different organizations.


Consensus information can be incorporated during the combination of the output (n-best list of full parse trees following distinct annotation standards) of individual parsers.

despite the success of n-best combination methods, they suffer from the limited scope of n-best list.


It is therefore necessary to consider the simplification process as a combination of different operations and treat them as a whole.

most of the existing models only consider one of these operations.


And previous research on English SRL shows that combination is a robust and effective method to alleviate SRL's dependency on parsing results (Màrquez et al., 2005;Koomen et al., 2005;Pradhan et al., 2005;Surdeanu et al., 2007;Toutanova et al., 2008).

the effect of combination for Chinese SRL task is still unknown.


(2009) used sentences with golden segmentation and POS tags as input to their SRL system.

we use sentences with only golden segmentation as input.


Therefore, SO is more complementary to FO1 than other outputs.


Each token is only assessed once given a set value of n, so we do not suffer from early prefixes being assessed more often.

larger values of n do not take all tokens into account, since the last y tokens of an utterance will not play a part in the accuracy when y < n. Since we evaluate given a gold standard disfluency, this measurement has recall-like properties.


The model considers whether the utterance continuation after the disfluency is probable given the language model; the relevant bigram here is p(rr i+3 |w i ), continuing with p(rr i+4 |rr i+3 ).

under the incremental model, it is possible the utterance has only been read as far as token i + 3, in which case the probability p(w i+4 |w i+3 ) is undefined.


The GENIA tagger (Tsuruoka et al., 2005) is particularly relevant in this respect (as could be the GENIA Treebank proper 4 ).

we found that GENIA tokenization does not match the PTB conventions in about one out of five sentences (for example wrongly splitting tokens like '390,926' or 'Ca(2+)'); also in tagging proper nouns, GENIA systematically deviates from the PTB.


Existing systems which evaluate each passage separately against the question would view each passage as having a similar degree of support for either hypertrophic cardiomyopathy or aortic  stenosis as the answer to the question.

these systems lose sight of a crucial fact, namely, that even though each passage covers half of the facts in the question, (2.1 a) and (2.1 b) cover disjoint subsets of the facts, while (2.2 a) and (2.2 b) address the same set of facts.


It will give a very small score (typically 0) to x 4 , x 5 , and x 6 , because the passage says nothing about elephants having large ears.

some passage scorers may be mislead by the fact that the term "large" appears twice question and either one could align to the one occurrence in the passage.


Watson's final answer merging and ranking component considers a pre-defined set of features and applies a machine learned model to score each candidate answer.

since each candidate has multiple, and generally a varying number of supporting passages, we use a merger to combine passage scores for < candidate answer, passage > pairs into a fixed set of features.


A merger strategy that takes maximum across passages will choose M AX (s 1.1 , s 1.2 ) as the optimal supporting passage.

since these passages have complementary information to offer, it would be better to somehow aggregate this information.


In this paper, we only considered merging evidence across passages and question terms.

this may be easily extended to merging evidence across passage scorers.


POV differences are also related to work on sentiment analysis in natural language processing (NLP).

to most prior work in sentiment analysis, we are concerned only with objective language in this paper.


2 Both articles are objective and meet the neutral POV criteria of Wikipedia.

there is a POV difference between them: the French article is more positive than the Spanish article.


Second, reading and evaluating an entire document takes a long time for an annotator and would make gold standard creation expensive.

our units cannot be too small -e.g., words or phrases -because POV is a complex phenomenon that cannot be judged reliably at such a low level; the sentences about Napoleon in the introduction are examples for this.


For many objective statements, it is clear which POV class -positive, neutral or negativeapplies to them.

there is a certain subset of statements for which the decision is difficult.


We use light stemming to extract the stem: only frequent suffixes/prefixes are removed.

a word is reduced to its corresponding root by removing all affixes, not just frequent affixes (Al Ameed et al., 2005).


All differences in accuracy and F 1 between the classifier and the baseline are statistically significant at p < .01.

8 the differences in accuracy and F 1 between using BOW and n-grams features are not significant.


The immediate effect of the shortcomings of a BOW-based feature representation is an incorrect estimation of absolute POV.

since these effects are somewhat random and will in most cases not affect Arabic and English to the same extent, the BOW problem can also give rise to incorrect POV differences.


Both machine learning and knowledge-base approaches lie at the foundation of contemporary named entities extraction systems.

the progress in deploying these approaches on web-scale has been been hampered by the computational cost of NLP over massive text corpora.


Figure 1a shows that Stanford POS tagger has improved throughout the years, increasing its speed by more than 10 times between 2006 and 2012.

the current speed is twice slower than the SENNA POS tagger.


For example, we do not need to split a number like 1, 000.54 into more units whereas we need to split a comma-separated list of words.

tokenization is important as it reduces the size of the vocabulary and improves the accuracy of the taggers by producing similar vocabulary to the one used for training.


First row shows that, given chunked input, the classification phase is able to achieve close scores to the state-of-art classifiers.

given the chunks generated by SpeedRead, the scores drop around 9.5% in F1 scores.


In their compound translation task, they use a dictionary to avoid out-of-domain translation.

to address this problem, which frequently arises in domain-specific translation we decided to generate our own customised lexicon; which we constructed from the multilingual Wikipedia and its dense inter-article link structure.


do not mention unknown words specifically, the fact that they use a character-based classification model and tokenization indicates that they can handle unknown words and perform stemming on them.

they do not present any evaluation on unknown words specifically.


The calculation follows the formula of the Workshop in Machine Translation (Callison-Burch et al., 2012), in order to be comparable with other methods: Pairwise comparisons with reference translations and pairwise ties in the human-annotated test-set are ignored.

every tie on the machine-predicted rankings is penalized by being counted as a discordant pair.


In this respect, the two knowledge sources of the UMLS, semantic network and Metathesaurus, are semantically linked to structure the semantics of biomedicine.

the integration of several vocabulary sources into UMLS has been made using experts with a goal to create a semantic link among the different biomedical resources by preserving the semantics and terms in the original resources.


Moreover, these forums have also become effective sources of self-service, thus providing an alternative to traditional customer service options (Roturier and Bensadoun, 2011).

a major challenge in building a system for forum content translation is the lack of parallel forum data for training.


The phrase-table merging (PTM) technique outlined in Section 3 was developed to rapidly combine incremental and baseline TrMs to aid our iterative data selection method.

here we use it as an alternative technique to combine the in-domain and out-of-domain phrasetables.


The results show weighted linear interpolation to be the best-performing system for different datasets and language pairs.

the difference in the evaluation scores between the different combination techniques are mostly statistically insignificant.


Since perplexity or cross-entropy have low correlation with actual translation quality, sentences selected using such techniques are not guaranteed to improve translation quality.

the TQS method only selects groups of sentences which improve translation quality, which is our overall objective.


A multitude of text similarity measures have been proposed for computing similarity based on surface-level and/or semantic features (Mihalcea et al., 2006;Landauer et al., 1998;Gabrilovich and Markovitch, 2007).

existing similarity measures typically exhibit a major limitation: They compute similarity only on features which can be derived from the content of the given texts.


By following this approach, they inherently imply that the similarity computation process does not need to take any other text characteristics into account.

we propose that text reuse detection indeed benefits from also assessing similarity along other text characteristics (dimensions, henceforth).


Various parts of the source text have been reused, either verbatim (underlined) or using similar words or phrases (wavy underlined).

the editor has split the source text into two individual sentences and changed the order of the reused parts.


In this paper, we thus overcome the traditional limitation of text similarity measures to content features.

we adopt ideas of seminal studies by cognitive scientists (Tversky, 1977;Goodman, 1972;Gärdenfors, 2000) and discuss the role of three similarity dimensions for the task of text reuse detection: content, structure, and style, as proposed in our previous work (Bär et al., 2011).


In total, 50 texts out of 253 have been classified incorrectly: 30 instances of text reuse have not been identified by the classifier, and 20 non-reused texts have been mistakenly labeled as such.

the original annotations have been carried out by only a single annotator (Gaizauskas et al., 2001) which may have resulted in subjective judgments.


The newspaper article about the English singer-songwriter Liam Gallagher, for example, is originally labeled as text reuse.

our classifier falsely assigned the label no reuse.


Out of these, our classifier mistakenly labeled 759 instances of negative samples as true paraphrases, while 413 cases of true paraphrases were not recognized.

in our opinion the 759 false positives are less severe errors in our envisioned semi-supervised application setting, as user intentions and the current task at hand may highly influence a user's decision to consider texts as reused or not.


For the latter, we see great potential for improvements by including, for example, measures for grammar analysis, lexical complexity, or measures assessing text organization with respect to the discourse elements.

each task exhibits particular characteristics which influence the choice of a suitable set of similarity dimensions.


Note that, since we are using the contextsensitive lemmas for matching, one can think of that as matching words on the sense level.

aMIRaN was trained mostly with morpho-syntactic features and therefore achieves good performance in identifying the common lemma of a context-sensitive part-of-speech tag for every word.


They submit carefully constructed queries to a search engine that might yield potential parallel webpages.

such a procedure depends on the quality of the query strategy as well as the Web index provided by the search engine.


In the previous section, we demonstrated the utility of our multilingual crawler for languages with reasonable resources.

such data may not be present for several other language pairs.


Even though the RBMT system was tuned to the domain of the TM via domain specific lexical resources (Section 3.2), most of the errors appear to be due to the RBMT system's inability to pick the right term for the technical domain data set.

compared to SMT and SMT+SPE, both the RBMT and RBMT+SMT system seem to produce a significantly lower number of grammatical errors, according to our evaluators.


If they become popular, various additional, more-or-less ad hoc linked projects end up being piled upon the original simple design.

there are very complex theories that have been being developed for decades to take into account all of the possible phenomena of natural language but have not yet undergone the ultimate test of large-scale treebanking (e.g.


It is available for use through the Grammar Matrix customization system's web-based The Grammar Matrix core grammar provides support for a wide variety of semantic valences.

the present system only exposes simple intransitive and transitive valences to the customization page.


This work is similar in spirit to Bender's (2008) development of an implemented grammar for Wambaya (ISO639-3: wmb) based on the Grammar Matrix and a descriptive grammar.

that work focused on hand-development of the grammar and included a manually entered lexicon, in contrast to our work on automatically populating the lexicon for the implemented grammar.


In fact, it turns out that this indicator conforms well to what one would expect: higher-order n-gram models generate a better approximation of real texts.

no n-gram model is able to capture long-range semantic dependencies well, which we will exploit in our analysis techniques.


Given a tree T , this allows us to calculate up the tree, even though it is through a downwards pointing conditional model, so the ith element of p can be predicted by: It is important to note that here c 1 , c 2 and p are binary sampled values, not their probabilities or averages, whereas the resulting probabilities are distributions.

used as a mean-field approximation, Equation 12is essentially the same as that of the RNN.


Although there are only 22 species of bumblebees in the UK, this means that there are almost 500 bee comparisons to be generated, and the overall number of possible texts would be orders of magnitude greater when contextual information (e.g., based on location and time of sighting) is included in the feedback.

the NLG Thank you for submitting this photo.


The mix of positive and negative values for the first fifteen identifications show that neither of the groups are consistently more accurate than the other.

the last five identifications show continually positive values, indicating that the richer feedback received by Group B was beginning to take effect.


As users who received the richer Type 2 or Type 3 feedback submitted more records on average than users who received Type 1 feedback, it appears that increasing the richness of information provision through NLG feedback has a positive effect on return rates of participants to the website.

this analysis is preliminary and these figures are potentially skewed by the presence of a small number of dedicated volunteers in each feedback group.


In addition, recording for some of the children in the BRB corpus began as late as month 21 and for others as early as month 13, making the data available for some of the children hard to compare.

the Providence Corpus provides data for all of the children starting from month 16 at the latest and starting from month 11 at the earliest and thus constitutes a much more homogeneous data set.


The collocSyll model shows the most dramatic drop in performance, from around 75% at month 11 (973 utterances) to just above 60% for month 21.

both the colloc and the Bigram model start around 60% at month 11 but, for month 21, reach around 70% so that there is no single model that comes in third for all amounts of input size.


We use this resource for the corpus analysis because it allows easy categorisation of words according to their frequency and elegant presentation and interpretation of results.

in Section 5 this method is abandoned and relative frequencies are calculated based on occurrences of given words in the training corpus, so as to ensure that words not found in the above mentioned dictionary are also covered.


Theories of the coherence of discourse and discourse relations (Barzilay and Lapata, 2005;Byron and Stent, 1998;Hobbs, 1979Hobbs, , 1985Mann and Thompson, 1988;Marcu and Echihabi, 2002) have proved useful for the semantic interpretation of discourse.

in the world of Twitter, Facebook, and other social media where people voluntarily join in the conversation, dialogue is often focused on the social engagements between participants.


the focus of the dialogue and its participants.

our early experimentation revealed that this straightforward approach of using Grosz and Sidner's framework with prevailing dialogue processing techniques fails to capture the complexities of human social interactions and is incapable of reliably inferring the social implicatures of dialogue.


Frameworks like DIT++ have extended the typical coverage of dialogue acts to encompass a boarder set of acts, such as social obligations.

when dialogue act schemes incorporate socially motivated acts often they do not fully take into account the multitude of purposes, social intentions, and ultimately the social implicatures of these acts.


The unigram and bigram (1+2-grams) based method performed the worst, mostly due to the size of data.

the gappy pattern approach was able to learn a mix of patterns of varying lengths and gaps (up to 2) that were able to separate the social acts.


Wong and Dras (2009) investigated particular types of syntactic error: subject-verb disagreement, noun-number disagreement, and determiner problems, relating the appearance of these errors to the features of relevant L1s.

they reported that these features do not help with classification, and they also note that character n-grams, though effective on their own, are not particularly useful in combination with other features.


In pursuing this experiment, we were indeed able to adduce new information about combinatory possibilities in CPs (section 5).

our experiment also provides a cautionary tale with respect to diving into a corpus "blindly", i.e, assuming that mere statistical analysis will provide good enough results and any noise due to language particular considerations will simply wash out if the corpus is large enough.


This turns out to be due to data sparsity, even with a 7.9 million token corpus.

we were able to identify previously unreported information about highly productive combinations and gain further structural insights into the language, particularly due to our use of novel methods coming from the field of visual analytics.


Since Urdu allows quite a bit of scrambling and also allows the nouns to be scrambled away from the light verbs, it was clear from the outset that we would not necessarily net all of the instances of N+V CPs that occur in the corpus.

we were not prepared for the amount of false hits we did get.


We found that a number of five clusters minimized the average distance between the nouns and the cluster centers.

both the table of numbers as illustrated in Table 2 as well as the clusters were difficult to evaluate in this form.


One preprocessing step that we could have done is to run a normalization module across the corpus.

this also requires specialized knowledge about the language/orthography and this source of errors was not large enough for us to take this step.


One is a normal space, the other is zero-width non-joiner (HTML code: ).

authors are not always consistent in their use, thus giving rise to errors in the corpus, which we again cannot deal with without adding time-consuming manual inspection coupled with deep language-particular knowledge to the process.


On the one hand, they employed lexical features, such as function words, frequently used character-based uni-, bi-, tri-grams as well as rare and most frequently used POS bi-and tri-grams.

they used three syntactic error types as features: misuse of determiners as well as subject-verb and noun-number disagreement.


Based on these results, Brooke and Hirst (2011) argue that a strong content bias is present in ICLE, allowing an easy classification by topic instead of by native language.

it remains unclear whether the poor Lang-8 results are not due to the properties of this specific corpus, which seems to be highly heterogeneous and incoherent, and whether the poor cross-corpus evaluation results are of general importance or due to the nature of the Lang-8 corpus.


At the end of Section 6, we hypothesized that the more abstract OCPOS-based n-grams may perform better than the surface-near word-based ones in cross-corpus evaluation.

the accuracies obtained using word-based n-grams are on average as good or better than the ones obtained using OCPOS-based n-grams (see Figure 6 and Table 4).


For this purpose we conducted a second set of experiments comparing single-corpus and cross-corpus results.

to their cross-corpus findings using the Lang-8 corpus, our results show that the patterns learned on ICLE do generalize well to other learner corpora.


91.53%, 87.5%, and 92.98% was observed for Person, Geo-political, and Organization type entities in the TAC2010 data (Ji et al., 2010) -in spite of a sense repository which is a priori quite vast.

the task of linking whichever concept mentions appear important in a corpus of very small documents should prove difficult, as it is more demanding in spite of a dearth of contextual evidence.


one that could, in principle, appear in Wikipedia).

the mention may refer to a valid concept, but there is not yet a corresponding Wikipedia page (see (Lin et al., 2012) for further discussion).


A number of well-known probabilistic topic modeling approaches such as Probabilistic Latent Semantic Analysis (PLSA) (Hofmann, 1999) and Latent Dirichlet Allocation (LDA) (Blei et al., 2003), have been explored to discover topics from a set of documents.

due to the shortness and lack of context, these topic modeling approaches may not work well with tweets.


Previous researches have shown that sentence compression can be used effectively in automatic summarization systems to produce more informative summaries by reducing the redundancy in the summary sentences (Jing, 2000;Knight and Marcu, 2002;Lin, 2003;Daumé III and Marcu, 2005;Zajic et al., 2007;Madnani et al., 2007;Martins and Smith, 2009;Berg-Kirkpatrick et al., 2011).

most of these researches either focused on the task of single document summarization and generic summarization or did not consider global properties of the sentence compression problem (Clarke and Lapata, 2008).


Here, all possible senses of a word are used as the alternatives.

in our ESSK formulation, we consider each word in a sentence as an "alphabet", and the alternative as its disambiguated sense found through a dictionary based disambiguation approach.


They propose a user model for jointly generating keywords and questions.

their approach is based on generating question templates from existing questions which requires a large set of English questions as training data.


The generated permutations provide alternatives to the original and thus the cover sentence can play the role of an information carrier.

not all the permutations are grammatical and semantically meaningful.


One difference between the proposed embedding method and the translation-based embedding methods is that the former restricts the length of a permutation to be the same as the cover sentence so that the receiver is able to recover the list of permutations; while the latter allows a permutation to only include a subset of the input words and therefore provides more choices for a given cover sentence.

dropping words introduces the risk of deleting information in the cover sentence and may lead to incoherence in the resulting text.


All the features we have described thus far have focused on sentence-level extraction.

document-level information also plays an important role in event extraction task.


Generally phrase-based SMT models outperform word-based ones (Koehn et al., 2003).

an SMT system fails to capture long-range contextual knowledge due to the limited horizon and the sparseness nature of lexical n-grams.


In this example, it correctly translates the diagnosis term "crystal induced arthritis".

it mistranslates its nearby phrasal verb "suffered from" by translating these two words separately.


Building an SMT system from large scale bilingual data for a specific application has become a practical option today.

sMT model heavily relies on the statistical evidences in the training corpus.


In our experimental domain (i.e., English medical summaries), bilingual medical dictionaries are available from plenty of resources and thus they are sufficient for collecting bilingual terminological units.

bilingual syntactic units are relatively hard to obtain.


Microblogging services have brought users to a new era of knowledge dissemination and information seeking.

the large volume and multi-aspect of messages hinder the ability of users to conveniently locate the specific messages that they are interested in.


Over time, a tremendous number of messages have been accumulated in their repositories, which greatly facilitate general users seeking information by querying their interested topics using the corresponding hashtag.

users often have to browse through large amount of results in order to find the information of their interests.


This observation verifies that our method is more stable in less training data.

our method fails for certain categories such as the Business and Education categories in Twitter dataset.


The approach we took for the generalized 2-gram models, and the hitting 2-gram model is the same.

the derivation for the value in our decision rule does not work out exactly, and only gives us a rough approximation of the probabilities.


This is why the classification accuracy for Dutch did not suffer as much as it did for Portuguese.

while the average testing document length for Chinese and Japanese is very short, we trained the algorithms with far more documents for these languages, and so the classification accuracies did not suffer.


How we count the number, type, and scope of errors in the learner's sentence depends on the relation between what was written and the annotator's correction.

on the task of error detection, how we score the performance of an NLP system depends on the relation between the system's output and both the learner's sentence and the annotator's correction.


3 EDMs can only work from the smallest unit they define, and in general the units seem to be variable.

finally, one benefit of EDMs is that they easily fit into calculations of P and R. by defining matches in terms of string distance, there is no way to talk about true negatives.


Among these, linguistically annotated corpora such as GENIA (Tateisi et al., 2000;Kim et al., 2003) have proven to be central to the NER solution.

due to the size of the vocabularies involved, annotated corpora by themselves do not provide a complete solution.


In summary it is thought that with partial matching, for the entity types examined so far, the core part of the entity was in most cases correctly found.

strict matching places too much faith in arbitrary choices in annotation guidelines.


They performed experiments which show that AZ can be used to identify and summarize novel contributions as well as background information in a scientific article.

they did not investigate integrating AZ in an automatic summarization system.


The summary lengths were controlled by using the distribution of themes in the abstract to select a proportionate number of sentences from each theme.

scientific articles differ from law judgments because the documents are highly structured and contain "sections" which are defined by the authors.


For example, the contraction I'm was parsed as two lexical items by MICA: I and 'm.

'm was not recognized as a form of the word am-itself a form of the verb to be-but rather was analyzed as a distinct verb.


In fact, even on moderately complex inputs, the generator quickly runs into a combinatorial explosion, having so far prevented the grammar from being usable for any serious real-time NLG tasks.

upon closer inspection of the source of the inefficiency, it became quickly apparent that the observed performance problems are the result of a conspiracy of several factors, most of which can be subsumed under the notion of non-configurationality: • Relatively free constituent order to English, constituent order in German clauses is relatively free, permitting permutation of complements, including the subject, as well as interspersal of modifiers in pretty much any position.


Rather, all three are general purpose, phenomenon-oriented regression test suites.

there are some differences in the design of the individual test suites that we expect to affect the impact of our performance improvements: while the MRS and TSNLP test suites consist of rather short utterances (MRS: 4.44 words/item, TSNLP: 4.76 words/item), Babel is slightly more complex (6.76 words/item).


We follow Faigley and Witte (1981) and define the top level layers Surface and Text-Base, which differentiate between meaning-preserving and meaningchanging edits.

contrary to Faigley and Witte (1981), we do consider all deletions and insertions of text as Text-Base changes.


Although we can assume that the FAs in our corpus have high quality, the NFAs show a broad quality spectrum according to the ratings by the WikiProjects' quality assessment teams, ranging from Start-to Good-class articles.

none of the NFAs have been rated with the highest quality scores, namely featured or A-class.


12), which does not come close to capturing the diversity in human query constructions or web scale.

we provide empirical results on 5000 queries from three query datasets with different noise levels.


Thus, W is constrained using the probability distribution R as: The singular variable W takes values from the universe of discourse U, such that values of W are singletons in U.

the semantic form of W is a variable whose values depend on the granular collections in U.


Similar to the approach presented here, Claveau & Kijak (2011) use translation equivalences between morphemes to generate translations and can handle fertility.

it is not suited for comparable corpora since it requires domain-specific parallel data (in their case, a multilingual terminology) to learn alignment probabilities.


Here, tweets are to a certain extent analogous to sentences in traditional extractive document summarization, which has been extensively studied in past decades (Ani Nenkova, 2011).

we argue that summarizing tweets is substantially different from summarizing news documents owing to the following reasons.


LexRank and TextRank make use of pairwise similarity between sentences, hypothesizing that the sentences similar to most of the other sentences in a cluster are more salient.

to the single level PageRank in LexRank and TextRank, MRC considers both internal and external constraints on three different levels, document, sentence, and term and achieves promising improvement.


A straightforward method would be to select the top-ranked tweets of the sub-topics.

this method would generate a redundant summary both at the tweet and sub-topic levels.


Low quality tweets account for 87%, which makes it difficult to achieve a very good prediction.

our concern is the performance on tweets of high quality because we want to select good tweets for summary generation.


Studies regarding vocabulary knowledge of second language learners have been mainly focusing on two major tasks: devising methods for measuring the size of the second language vocabulary of learners for testing purposes (Schmitt et al., 2001;Laufer and Nation, 1999;Nation, 1990) and determining the words that the learners should learn (Nation, 2006).

there have been few studies on what kind of words learners actually know.


Therefore, a number of machine learning methods, such as a support vector machine (SVM) for the binary classification task, can be used as predictors.

to answer our research question, what kind of words learners actually know, we want predictors to be able to do more than just predict.


This can be seen as filling in the blanks of a learner-word matrix.

the out-of-sample setting support new words, i.e., some or all words in the test data are missing in the training data.


Some interpretable weight vectors can determine word difficulty.

the perceived difficulty of a word differs from learner to learner.


Joint disambiguation and clustering enables us to exploit such connections: knowledge about which mentions refer to the same concept can support disambiguation decisions.

disambiguation influences clustering decisions.


On the other hand, disambiguation influences clustering decisions.

local approaches which disambiguate mentions independently of each other (Milne and Witten, 2008;Csomai and Mihalcea, 2008) can not take advantage of such relations.


The more distant a preferred name is from a mention, the less likely it is that the mention refers to this concept.

to local features, global features involve more than one mention.


Given our approach we disambiguate the whole document and not just the query terms.

to ACE 2005 the NILs are not just annotated as NILs but also clustered, which allows us to evaluate the entity clustering performance in a direct way and not just its influence on the disambiguation performance as on the ACE data.


This further supports our earlier discussion that our features as a representation type of coordination is more useful than the "conjunct is the head" representation used in the Penn2Malt conversion.


The results show that the manual taxonomies have high quality well defined relations.

the novel automatic method is found to generate very high cohesion.


these studies often don't answer certain questions about the taxonomy.


To turn the flat LDA topic model into a navigable hierarchy, Griffiths and Tenenbaum (2004) describe a hierarchical LDA approach.

this was found to be prohibitively time consuming given our large data-set.


A problem with some of the manual taxonomies is the very high number of top level nodes, which makes it difficult for users to browse.

there is no obvious way to select suitable top level nodes in these taxonomies.


The results (Table 2) show that most of the taxonomies achieved roughly the same level of cohesion for the clusters, roughly between 50 and 63%.

the WikiFreq taxonomy performed far better, with only one unit of the 30 judged as not coherent.


This might be explained by considering that items grouped together under the same node will share a number of keywords which link to the same Wikipedia articles which would ensure that the items are very similar.

the Wikipedia taxonomy and DBpedia ontology use categories rather than articles in Wikipedia as the concept nodes.


The LCSH taxonomy has been manually created for the purpose of organising library collections and so might be the obvious choice to organise CH data online.

the results show that the relations within LCSH are defined less clearly than that of the Wikipedia derived taxonomies.


The tagset being composed of two tags, this dimension is also at 0 and the expressiveness of the annotation language is 0.25 (type language).

the ambiguity degree is high (1) as all occurrences are ambiguous.


Delimitation is 0, as gene names, in our example, are simple tokens.

the characterizing factors are low: the tagset is boolean (Dimension=0), a type language is used (Expressiveness=0.25) and ambiguity is very low as only few gene names are also common names (theoretical ambiguity can be approximated at 0.01 9 and residual ambiguity is on average of 0.04 for two annotators).


A limitation of CLRLM is that it depends either on a parallel corpus or on a bilingual dictionary.

for low resourced languages, parallel corpus seldom exist and dictionaries have poor vocabulary coverage.


we refer to as pseudo-relevant document sets from now on, can thus potentially replace the parallel corpus requirement of the CLRLM.

it is impractical to assume a one to one document level alignment between the pseudo-relevant documents in the two languages.


Thus, a natural question which arises is whether we need to introduce a new linear combination parameter to choose the two event paths with relative weights similar to (Chinnakotla et al., 2010).

a closer look at Equation 3 reveals that the contribution from each path is inherently controlled by the two coefficients P(w T |z T ) and P(w T |w S ), thus eliminating the need for an extra parameter.


In this work we present a novel approach to bootstrap domain specific terminology, namely Structured Term Recognition, and we apply it to the medical domain.

to previous approaches, based on observing distributional properties of terminology with respect to their contexts, our method analyzes the "internal structure" of multi-word terms by learning patterns of word clusters.


Combinations of STR and techniques based on contextual information are obviously possible.

in this paper we decided to focus on the contribution of the STR technique in isolation because we want to show that in the medical domain this information is a strong indicator of the entity type if well represented and handled.


loot /lu:t/, looted /lu:t1d/ in Sanskrit, where oral tradition dominated the sphere of learning and an advanced discipline of phonetics explicitly described prosodic changes, these prosodic changes, well known by the term sandhi, are represented in writing.


Once an SH word was matched to all the possible MW headwords, the matching values were sorted and the headword with the highest match was marked as the suitable match.

if the top two headwords have the same matching score (this also includes the case, where all the headwords obtained a score of 0.0), the SH headword along with all the MW headwords and their meanings were dumped in a text file interface, where the exact match was decided manually.


Our analysis shows that the performance for in-domain data is largely dependent on the characteristics of the translation model.

performance in out-of-domain tasks relies on characteristics such as reordering and alignment distortion.


Regularization helps in part to alleviate over-fitting.

we performed several tests to ensure that over-fitting was not a problem.


More data needs to be analyzed in order to make more reliable estimations.

we should note that there is a consistent agreement between features that are important for BLEU, Meteor and TER.


Some of the conclusions from these models are straightforward and match the empirically developed intuition.

the insight gained from this type of analysis can be valuable for designing new systems for new translation tasks.


The classifiers trained on language model features (LM) and syntactic features (SYN) proved to be less effective predictors when taken on their own.

they proved to be valuable when combined with other feature groups.


Recent methods (Mitchell and Lapata, 2008;Erk and Padó, 2008;Thater et al., 2010) exploit direct syntactic/semantic relations with target words, but these methods would still fail in the above examples, because the direct structural neighbors of banks are the same in the two contexts.

syntactic/semantic structural co-occurrences of multiple words, obtained from the dependency relations, seem effective for this example.


We can regard a local context vector as representing a particular meaning the target word conveys in the given context.

these vectors may be too sparse to provide enough information, especially when the available context is short.


Naively enumerating all walk pairs (z, z ′ ) in Eq.

3is intractable when L is large, because the number of possible walks grows exponentially with length bound L. for the specific weight function p(z | w) given by Eq.


Desrosiers and Karypis use a random walk model that do not cast a bound on the length of walks.

our model poses a strict upper bound L on the walk length; this model was chosen because co-occurrences comprising hundreds of words are unlikely to be effective for contextual word discrimination.


Tree kernels are hence not suitable for measuring word similarity.

the bag-of-walks kernels distinguish target words from the other words in the sentence, as they only count walks starting from the target words.


Like bag-of-walks kernels, their kernel takes multi-word co-occurrences with a target word into account, but these co-occurrences are taken in terms of (gap weighted) n-gram collocations; i.e., their kernel counts the overlap of n-gram sequences surrounding the two target words.

bag-of-walks kernels compute multi-word co-occurrence in the syntactic/semantic structure present in parse graphs.


Like other languages, Japanese uses sentences composed of words.

we can also say that a Japanese sentence is composed of bunsetsu.


Suppose that we want to drop the word "kokusai" (international) from the noun and to shorten the bunsetsu to "kyoudou kenkyuu guru-pu ga".

bunsetsu-based methods cannot perform such flexible word selection because they are limited to unit constraints.


When dependency constraints were not considered, PROP performed better than BNST.

the performance differences between them were small and only the difference in ROUGE 1 was statistically significant (Wilcoxon signed-rank test, p < 0.05).


However, the performance differences between them were small and only the difference in ROUGE 1 was statistically significant (Wilcoxon signed-rank test, p < 0.05).

when dependency constraints were considered, PROP significantly outperformed BNST.


In this way, PROP w/ DPND selected more bunsetsu that contained important words and achieved higher performance than BNST w/ DPND.

when dependency constraints were not considered, the number of (shortened) bunsetsu that PROP selected was not so different from the number of that BNST selected (the difference was 2.4%).


BNST w/ DPND could not perform such an operation because the method has to treat each bunsetsu as it is.

pROp w/ DpND could shorten the bunsetsu by dropping word " ", which had less importance, from the bunsetsu (Scor e(" ") was 0.119).


With a best value of 38.02 % v-measure it seems that we simply cannot recreate the classes that our theory predicts.

it is worth taking a closer look at the results for the individual classes, since it turns out that the clustering quality varies greatly with the class that we are trying to produce.


We can conclude that argument structure, among others, is a fairly good indicator for automatically identifying the combined class {MAN,CRE}.

for the individual classes MAN and CRE, as well as for the directional class DIR, this is not the case.


The methods in this category consider both the query and the document.

an implicit assumption in this method is that the document to be summarized is relevant to the given query.


The ultimate measure is to put snippet directly in a search task and evaluate how well they can help accomplish the task (Tombros and Sanderson, 1998;White et al., 2003).

this method is very expensive and not reusable, and the utility measure is influenced by both the retrieval performance and the quality of snippets.


This means that these methods have a higher impact on top retrieval results.

we did not observe a significant difference when the snippets of the search engine are used.


This categorized lexicon is quite similar to word cluster features in (Ratinov and Roth, 2009), but is more precise.

to our certain knowledge, this resource remains unexplored in previous Chinese NER tasks.


After adding this the F-score on testset goes up from 94.1% to 94.4% for SGD and MADF, 95.5% for ADF, comparable to CRF++.

on a different dataset CWS PKU, adding this "F" feature decreases F-score by 0.1%, still higher than CRF++ by 0.3%.


The classification accuracy at 10K is 75.07%.

after that, the pace of improvement is very gradual.


For supervised methods, summarization is often regarded as a classification task or a sequence labeling task at sentence level, and many supervised learning algorithms have been investigated including Hidden Markov Models (Conroy and O'leary, 2001), Support Vector Regression (You et al., 2011), Factor Graph Model (Yang et al., 2011), etc.

such a supervised learning paradigm often requires a large amount of labeled data, which are not available in most cases.


(Wang et al., 2011(Wang et al., , 2012 proposed a Bayesian interpretation to assess tweet credibility.

it remains as a preliminary approach due to the linear assumption made in the iterative algorithm of the basic fact-finding scheme.


Combinatory Categorial Grammar (CCG) is an expressive grammar formalism which is able to capture long-range dependencies.

building large and wide-coverage treebanks for CCG is expensive and time-consuming.


the CCGbank (Hockenmaier and Steedman, 2007).


The generative process generates grammar rules independently given their parents, without regard to the lexical information.

the constituents and contexts have been proven useful for grammar induction (Klein and Manning, 2002;Headden III et al., 2009).


The lexicon for atomic categories remains fixed after the initial lexicon (0) has been created.

the POS tags may acquire more syntactic categories in the lexicon generation stage.


One additional induction step they used is the "derived" induction step, in which adjacent constituents that can be derived from the existing lexicon are combined.

their experiments do not show significant improvement of this lexicon generation method, so we omit this step in our experiments.


For instance, for span 〈2, 4〉 of tree in Figure 1, we model P(VBD_RB|S) rather than P(VBD_RB|B).

this category-dependent boundary model performs poor in experiments (not reported in this paper).


This practice may reduce data sparsity problem caused by directly modelling words.

this may also lose useful lexical information.


The graph sequence d is represented by The order of graphs g ( j) represents the order of the graphs in an observed sequence.

the order of graphs g ( j,k) is the order of graphs in the artificial graph sequences, and there can be various artificial graph sequences between graphs g ( j) and g ( j+1) .


Regarding inference, our method may seem related to the coreference resolution research based on Markov Logic Networks (MLNs) (Poon and Domingos, 2008;Song et al., 2012).

previous MLN-based work on coreference resolution does not incorporate inference rules based on world knowledge.


Traditional general readability methods (Dubay, 2004) have been applied to several problem tasks such as matching books with grade levels (Collins-Thompson and Callan, 2005;Fry, 1969).

the problem of readability has not been well explored in Information Retrieval (IR) (Kim et al., 2012).


The Flesch reading ease score for this sentence is 62.11, which according to the score is not a difficult sentence.

the sentence carries a deep technical meaning which requires domain-specific knowledge for proper comprehension.


In (Nakatani et al., 2010) the authors used Wikipedia to build a list of some technical terms.

our proposed framework in this paper does not require an ontology or seed concepts, which can be regarded as a major innovation.


Language modeling approaches cannot capture domain-specific concepts in a domain (Zhao and Kan, 2010).

our method does not need any annotated data.


Hence a document comprising of many domain-specific terms will be difficult to read and also if the terms are not related to each other (low cohesion) in the same document then the reader will face difficulties in relating different concepts of a domain (Yan et al., 2006).

the computation of document scope and cohesion in (Yan et al., 2006) is accomplished using an ontology tree which requires an ontology for every domain.


In (Park et al., 2002), they named a domain-specific term extraction scheme as Degree of Domain-specificity.

their method deals with a completely different problem task.


• Wordnet-head-hypernyms-match: Some closely related words (like epistaxis and hemorrhage) do not share any common synset.

if we consider the parents (or hypernyms) of the synsets of such words in the Wordnet hierarchy, we can see that the two words are similar.


Thus, there are clear potential benefits to fine-grained citation analysis; and a number of case studies have been published that demonstrate this potential (Nanba et al., 2004;Teufel et al., 2006b) [CEPF] .

fine-grained citation analysis is currently not widely used in applications that access and analyze the scientific literature.


This means that the development cycle for a citation classifier must be started from scratch for each new application.

to this prior work, we base our work on a standard classification scheme for citations from information science, the classification scheme of Moravcsik and Murugesan (1975) [CERF] (henceforth MM).


The NER features we extract are related only to the NLP domain.

this approach for acquiring named entities is not domain dependent and can be used to develop a reasonably efficient NER system using lists of tools or resources from any domain.


Such syntax-based SMT systems can automatically extract larger rules, and learn syntactic reorderings for translation (Yamada and Knight, 2001;Venugopal and Zollmann, 2006;Galley et al., 2004;Chiang, 2007;Zollmann et al., 2008;DeNero et al., 2009;Shen et al., 2010;Genzel, 2010).


Our syntax-driven approach to rule extraction is inspired by (Chiang, 2007(Chiang, , 2010, while the canonical grammar approach is based on (Galley et al., 2004(Galley et al., , 2006.

we induce synchronous graph grammars between surface form and meaning representation, instead of transfer rules between source and target form.


As with other translation work using synchronous tree grammars, such as synchronous TSG (Chiang, 2010) and synchronous TAG (DeNeefe and , our SHRGs can also be applied in both directions.

none of these SMT approaches use an intermediate semantic representation.


This generates a correct statement, knowledge of which is the point of the question.

if (7b) is used as an inverted question, "Spain" is returned by DeepQA as the first answer.


This function enables visually impaired people to use e-mail, read news, view Web pages, and operate other complex applications.

the existing screen readers use some distinctive explanations that do not make a target kanji easily identifiable, such as "aya for 1 aya-ori ( , twill)" for " (aya)."


Since the remaining two identified the correct kanji by using distinctive explanations generated by the second step, we confirmed the effectiveness of the proposed two-step method.

only six subjects identified kanji from distinctive explanations of the screen reader.


For " (kei)," our system used ambiguous words, and most subjects failed to identify the kanji 7 .

the screen reader achieved a high identification rate for this kanji, by using the distinctive explanation "Write on top of (tsu-chi, soil)."


The rule-based system described in Section 2.3 achieves good results.

it, like other rule-based methods, has some shortcomings.


In some cases (Lemma, Phenotype candidates, NP start and NP end, and Physiology) ignoring the feature causes small improvement in precision or recall.

the F-score is always less than the F-score of final results.


NP boundaries and semantic types are features used by both methodologies, so the errors made by MetaMap have effects on the performance of each system.

the rule-based system is more dependent on MetaMap output and errors in MetaMap output changes the results completely.


The word "associations" is the head of this phrase, so the semantic type [Mental Process] is assigned to it and it is not tagged as a phenotype name by the rule-based system.

there are some cases in which MetaMap assigns the correct semantic type to a phrase and found a good boundary for a phenotype name but the machine learning method does not mark it as a phenotype.


One possible reason may be due to the generation of the Fanse scheme over the NP-enriched Penn Treebank.

the C&C parser was trained over a non-enriched version of CCGbank where all the noun phrases are right-branching.


We induced representations of d = 40 dimensions for input vocabularies of |V en in | = 43, 614 and |V de in | = 50, 110 words (filtering out words which occur fewer than five times in our dataset).

to speed up training, 5 we learn on a subset of training sequences choosing the 3,000 most frequent words in en and de for their output vocabularies V en out and V de out , respectively.


As a representative to linguistic studies on event anaphora, Asher (1993) proposed a discourse representation theory to resolve the references to events.

no computational system was proposed in his work.


On one hand, since the antecedent candidate is an event trigger and the anaphor is a pronoun, both carry little obvious information about their own.

the event anaphor and candidate pair in event pronoun resolution consists of a predicate and a pronoun.


For example, one day cricket match is <<one-day>-<cricket-match >> and South Indian Association is <<South-Indian>-Association>.

since the surface forms for adjectival usage and compounding forms in English being the same, one may have ambiguous expressions such as South sea route.


a 'tree' and sākhā 'branch' is part-of (avayava-avayavi).

in all the three cases instead of specifying these deeper relations, relation between the components is expressed through the genitive case suffix in the paraphrase of these compounds as rājñah .


In case of English-Hindi language pairs, it was observed that in 59% of cases an English Noun compound can be translated into genitive construction in Hindi (Paul et al., 2010).

for other NLP tasks such as information extraction, question answering etc., genitive relation will not be sufficient and one needs to look for deeper semantic relation.


In recent years, the research field of sentiment analysis has focused on analyzing this form of textual-information, particularly opinions or sentiments expressed by internet users.

given the international nature of the web and online shopping, opinions in a user's mother language may not be available.


One way to solve the WTD problem is to calculate the sum of association scores of pairs among translation of the target word and all its surrounding words' translations, and then select the one with the highest score.

since the different surrounding words have different amounts of influence on the target word, it is necessary to add some weighting factors (e.g., word distance).


These approaches rely on large parallel corpora to train a WSD classifier.

for some language pairs (e.g., Japanese-Chinese), such corpora are not available.


That is why DeSCoG+ does not perform significantly better than DeSCoG.

the fact that the recall of DeSCoG is nearly unchanged when the training data size is over 5000 suggests that there are some structures that the alignment phase missed even when more training data were used.


Secondly, incorrect syntactic parses could lead to incorrect (or incomplete) MRs. For example, with the incorrect syntactic parse shown in Figure 8, it is very difficult to build the correct MR answer(A,(state(B),next_to(A,B),const(B,stateid(arizona)) because one of the two words state and Arizona has to receive the meaning next_to(A,B), which is very unlikely.

parsers which learn parsing syntax and semantics simultaneously could easily overcome this problem when they recognize that the word border should be a head of two dependents.


Looking at the sizes, the optimal seed word list length seems to be 5,000, because that is the one that obtains the largest corpus.

the type of documents from which the corpus has been built is something to take into account, which is shown in fig.


The optimal word-combination length to send to the APIs seems to be 2, because it obtains the largest and most varied corpus with the least number of PDFs.

if more than 100-150 million words are needed, crawling is the way to go: we have collected a corpus of a size and website variety comparable with those obtained via search engines, with much fewer PDFs and the potential to get much bigger.


EBMT systems can usually only handle a moderate size example base in the matching stage.

using a large example base is important to ensure high quality MT output.


One thus has to strive for designing a similarity function which produces rankings as close as possible to the ranking as computed by the LD computation.

this proves difficult because standard IR similarity scores work on the principle of material similarity, i.e.


Simple retrieval methods such as raw term frequency (tf ) or tf-idf do not perform well for ASM.

to standard IR, stopword removal and stemming decrease performance.


This method works well for non-standard tokens that are generated by a few simple operations such as substitution, insertion, and deletion of letters of the standard words.

it cannot handle words such as 'ate' (meaning 'eight'), where non-standard tokens are created based on the phoneme similarity with the standard words, or 'bday' (meaning 'birthday'), which involves too many operations.


Character-block level sequence labeling can successfully normalize non-standard tokens such as 'gf ', 'prof', and 'pro' (to 'girlfriend', 'professor', and 'problem' or 'probably').

these cases are very hard for the MT systems to tackle with.


One important reason is the training data size -ours is much smaller.

our system uses fewer candidates and is more efficient than (Liu et al., 2012), which considers all the English words with the same initial character.


We can see that the performance of the character-block level MT improves steadily as the training data grows.

the training size effect on the character-level two-step MT is rather small.


Under the framework of extractive summarization, it is important to acquire the relationship between sentences and aspects for sentence selection.

in most existing HDP models, the sentence level is disregarded and we cannot directly get the aspect distribution of sentences.


If the pseudo trigger mention is similar to one STM, their similarity will be high.

instead of calculating the average similarity, we calculate the maximum similarity to identify whether the pseudo trigger mention should be filtered out: where n is the number of STMs for the pseudo trigger mention p and ) , ( the global discrimination comes from the probability of a pseudo trigger mention belonging to the set of true trigger mentions in the training set.


Since domain dictionaries are frequently available for NLP related projects (e.g., technical manual translation), they can thus provide big help in real applications.

the above dictionary related feature utilized in discriminative approaches (Low et al., 2005;) cannot be directly adopted by a generative model 3 .


Therefore, this candidate-feature is associated with each candidate of the position-tag.

if no dictionary word covers this character, then 2 TM will be set to "Inapplicable" regardless of which tag is assigned to "学" (i.e., we do not want to disturb the original model in this case).


Equation (3) weighs the tag matching factor and the character-tag trigram factor equally.

it is reasonable to expect that they should be weighted differently according to their contribution.


Afterwards, they further proposed an integrated model to integrate generative and discriminative approaches, as these two approaches complement each other.

dictionary information has been utilized in the discriminative approach in the previous works of (Low et al., 2005;.


On the other hand, dictionary information has been utilized in the discriminative approach in the previous works of (Low et al., 2005;.

they focus on improving the in-domain word segmentation accuracy, while we investigate how the domain invariant feature (based on dictionary information) helps for cross-domain tasks.


First, we do not simply add the dictionary matching information as an additional feature under the Maximum Entropy framework.

we derive a new generative model with dictionary information starting from the problem formulation, and solve the problem in a principled way.


Recent study shows that parsing accuracy can be largely improved by the joint optimization of part-of-speech (POS) tagging and dependency parsing.

the POS tagging task does not benefit much from the joint framework.


(Hatori et al., 2011) propose the first transition-based joint model for Chinese POS tagging and unlabeled dependency parsing and gain large improvement in the parsing accuracy.

their joint models only slightly improve the tagging accuracy over a sequential tagging model.


The representative methods include TagLDA (Krestel et al., 2009;Si and Sun, 2009) and Content Relevance Model (CRM) (Iwata et al., 2009).

these methods usually suffer from the over-generalization problem.


WAM-based methods, formalizing trigger probabilities at word level, consider each single word in document and project from document content to keyphrases.

the coverage of document themes should be appreciated at topic level, which is beyond the power of WAM-based methods.


On the one hand, parsing barely based on bi-lexical features without POS tags hurt performance dramatically.

if we tag the whole sentence first and then do dependency parsing, then syntactic features which are demonstrated to be effective for tagging disambiguation cannot be utilized.


These results demonstrate that our training method can bias the joint model towards the desired task.

as we try different losses, tagging accuracy rarely changes.


This may due to the fact that our joint decoder is deterministic thus suffers more from error propagation comparing with beam search based or dynamic programming based decoders.

our joint method can also be enhanced with beam search and we leave it to future work.


Reading is a common activity that is part of the process of information transfer between humans.

despite of the important role it has played in recent history and its current wide use, this process of information transfer is not well understood.


For example, the factors to measure success when reading a document with the objective of writing a review or preparing a presentation are clearly different.

for the sake of simplifying and unifying our method to measuring task performance, we will resort to measuring the level of understanding of subjects after reading a text.


Although this also involves a disambiguation of translations, their work is not directly comparable to ours, since they do not strictly use the word senses encoded in Wiktionary but define them based on the translations shared across multiple languages.

to that, we aim at exploiting a wide range of lexical-semantic knowledge and therefore need to rely on the word senses actually encoded in Wiktionary.


The disambiguation of translations hence seems to be more difficult for our raters.

the κ scores are well above .67 and therefore allow us to draw tentative conclusions (Artstein and Poesio, 2008).


Our DT-based expansion technique has no notion of dimensions since it works on the word level, and thus does not suffer from this kind of sampling error that is inevitable when representing a large vocabulary with a small fixed number of dimensions or topics.

while vector-space models do a good job at ranking candidates according to their similarity, 2 they fail to efficiently generate a top-ranked list of possible expansions: due to its size, it is infeasible to rank the full vocabulary every time.


The lexical expansions shown in Figure 1 were generated by the same DT used in our experiments.

for the general case, we make no assumptions about the method that generates the lexical expansions, which could just as easily come from, say, translations via bridge languages, paraphrasing systems, or lexical substitution systems.


Nov for November) or use of punctuation.

to their approach, where they discarded these cases, we used minimum edit distance (MED) to determine the re-generated candidate closest to the original.


The highest-ranked ones were believable as reliable instances of dependencies: infinitival to in VB TO seems likely to be often correct, as does existential there in the first three cases.

it is quite possible that many of the reliable ones such as PRP VBZ are actually poor at distinguishing between positive and negative examples by virtue of their frequency -they may occur equally often with both, in the same way that words like the are useless in general text classification.


Choosing features by a reliability-based measure did not prove useful.

this may be related to systematic choices made by the unsupervised parser that were different from the gold standard's choices, rather than bad parsing; an option for aligning systematic choices is the HamleDT approach and software of Zeman et al.


We employ two generative models (Sections 2.1, 2.2) to accomplish the first task of debate post classification and then generate the data for linguistic style experiments in Section 2.3.

before proceeding, we briefly review related work on debates.


In (Somasundaran and Wiebe, 2009), opinions/polarities which were correlated with a debate-side were used to classify a post as for or against.

this thread of research does not model agreements and disagreements in debates.


For most style dimensions, mutual accommodation across agreeing pairs is more than that for disagreeing pairs.

for 4 style dimensions, negation, exclusive, discrepancy, and 2nd person pronoun, we find the trend reversed (values marked in bold).


Theoretically, considering the following probabilistic expression: 10) when , ( ) > 0, we have the following two exclusive cases: Case 1: ( ⟵ ) ( ) > ( ⟵ ) ( ) Case 2: ( ⟵ ) ( ) > ( ⟵ ) ( ).


For span i S k , only n k can interact with the outside of the span: n k can be modified by n h (h < i), and n k can modify n l (k < l).

n j (i ≤ j < k) cannot be modified by n h or cannot modify n l .


They have proven effective as components of a wide range of NLP applications, and in the modelling of cognitive operations such as judgements of word similarity (Sahlgren, 2006;Turney and Pantel, 2010; Baroni and Lenci, 2010), and the brain activity elicited by particular concepts (Mitchell et al., 2008).


For example, it will not recognize the following coordination: eksportowane do Niemiec g en i na Litwę acc 'exported to Germany and to Lithuania'.

it excludes the majority of cases where two terms preceded by prepositions are separated by a comma and they belong to two different parts of a sentence, e.g: W <Polsce> loc , mimo <wpisania pojęcia konsumenta> g en do konstytucji ... 'In [Poland], despite of [entering the notion of a consumer] into the constitution ...'.


Less than 10% alignment error rate (AER) for French-English has been achieved by the conventional word alignment tool GIZA++, an implementation of the alignment models called the IBM models (Brown et al., 1993), with some heuristic symmetrization rules.

for distant language pairs such as English-Japanese, the conventional alignment method is quite inadequate (achieving an AER of about 20%).


They are often aligned to some words incorrectly.

with the conventional model, our model derives such function words from content words in their own language.


One solution, adopted by almost all the existing alignment models, is to align unique function words to NULL.

it is difficult to judge whether a unique function word has to be NULL-aligned or not, and often causes alignment errors as shown in Figure 1.


Finally, θ T can be decomposed as: The earlier study (Nakazawa and Kurohashi, 2011) only considered treelets as alignment units.

this is inadequate for semantic-head dependency trees, since a set of sibling function words is often considered as an alignment unit.


NER spans are therefore a subset of syntactic constituent spans, and the two problems can be represented with a single tree-structured derivation.

a joint model of prosody and parsing would be difficult to capture with this approach, as boundaries of prosodic and syntactic spans are not required to overlap (Selkirk, 1984;Jackendoff, 2002, Ch.


The bracket model cannot offer competitive accuracy when compared to the other models and has limited application given its output is only a set of projective spans.

coupled with an appropriate task it does offer exceptional parsing speed, decoding length 40 sentences at more than 10 per second on our test system, and may be useful as a component in select joint modeling tasks.


This is somewhat surprising given the sequential constraints of our NER model, but likely also due in part to the less difficult 4-label NER task reported in previous results.

we see stronger than average gains by coupling the models and using joint inference.


In recent years, error mining approaches have been proposed to identify the most likely sources of errors in symbolic parsers and generators.

the techniques used generate a flat list of suspicious forms ranked by decreasing order of suspicion.


Like in (Koller and Striegnitz, 2002), the initial FB-LTAG is converted to a grammar of its derivation trees.

in this case, the grammar conversion and the resulting feature-based RTGs accurately translates the full range of unification mechanisms employed in the initial FB-LTAG.


Supertagging was shown to improve the performance of symbolic parsers and generators significantly.

it requires the existence of a treebank in a format appropriate to generate the supertagging model.


In sum, various symbolic and statistical techniques have been developed to improve the efficiency of grammar-based surface realisation.

statistical systems using supertagging require the existence of a treebank in an appropriate format while the purely symbolic systems described in (Carroll and Oepen, 2005;Gardent and Kow, 2005;Koller and Striegnitz, 2002;Gardent and Perez-Beltrachini, 2010) have not been evaluated on large corpora of arbitrarily long sentences such as provided by the surface realisation (SR) task (Belz et al., 2011).


In the latter case an unambiguous placement pattern is typical, e.g., a main verb being placed immediately behind its auxiliary.

the placement of a main verb to resemble German depends heavily on the parser accuracy (clause boundaries), and it can sometimes be ambiguous (e.g., verbs may or may not move past embedded clauses).


As we shall see, these structures will use hierarchies which look rather like syntactic categories, but are quite different from traditional parts of speech or other treatments.

we note that there can be many grammars that explain a given set of sentences, and the grammar that describes this particularly input may differ substantially from human-crafted grammars.


Note that we make no assumptions on the category of words or distinguish them as nouns/verbs.

different machine learning algorithms are required for learning perceptual objects, events, and spatial relations; thus these are distinguished in the semantic space.


So far, we definitively demonstrated the utility of CSI features in guided summarization.

the previous experiments made use of gold-standard, human-assigned categories for each topic, provided manually by the TAC organizers.


Looking at "left" and "Sunday", it is quite straightforward to determine that they take place within the same time span.

it gets significantly more difficult when we look at "said" and "Sunday".


Methods proposed in this paper need further validation in large-scale corpus.

we will make more research on the Tibetan BaseNP templates using grammatical rules to produce a high quality results.


Therefore, the usage of the capitalization feature is not an option in Arabic NER.

the English translation of Arabic words may be exploited in this respect (Farber, Freitag, Habash and Rambow, 2008).


Indeed, adding the property trapped for the class Animals to Wikipedia, and filling in its corresponding values for all instances (kinds) of Animals, would likely have little value.


This is, of course, a positive behaviour when the system translates sentences from the same domain.

if this is not the case and the input sentences contain no or very few longer phrases from the translation tables, the system is not able to construct good translations from shorter phrases.


Our baseline systems, trained and tuned on general domain with maximum phrase length set to seven, translate general-domain test sets with an average phrase length of 3.49 (see Table 4).

for the systems tuned and tested on in-domain data, this score is as low as 1.80.


Specific to ranking problems, ensemble ranking or ranking aggregation methods have been widely studied in many different tasks especially in information retrieval.

there are limited work attempts on applying ensemble methods to summarization.


In order to identify the importance of sentences from multiple aspects, aggregation methods can be used in summarization to combine results from different summarizers.

aggregation methods for summarization are seldom been discussed in previous work.


For example, in our example thread, the message from Bill (first message) could be interpreted in isolation as a request from a peer or even a subordinate.

if you take into consideration that Barry delegated the task to Stephanie upon receiving the message from Bill, the first message could be considered as Bill assigning a task to Barry.


We will investigate using automatic taggers (such as a dialog act tagger and link predictor (Hu et al., 2009), an ODP tagger (Prabhakaran et al., 2012b)) to extract these features to predict power.

one main contribution of this paper is to show the interaction between these dimensions of the dialog (like dialog structure and ODP) and situational power, which is an important first step towards solving the problem.


In this sense, our approach is similar to theirs.

while their distance measure of dependency heterogeneity is limited to three easily-mapping common relationships between two languages, namely SUB, OBJ and NMOD, we further generalize to automatic mappings of any bilingual dependency relationships.


This trend suggests that there do exist some noisy mappings, more or less in both A Ψ and M Ψ .

removing them can only lead to limited performance improvement while too few mappings severely harm the performance.


Table 2), both sets of mapping are diverse without a dominant single mapping.

for adjectives dependency mapping amod-amod accounts for nearly 70% of total mappings.


Naively, one might think that simply replacing the simple clustering algorithm used by RR08 with a more elaborate approach would result in improved final categories.

as we show in Section 3, a variety of clustering approaches did not lead to a noticeable improvement.


(Goldwater et al., 2006;Johnson et al., 2007;Haghighi and Klein, 2007;Johnson and Goldwater, 2009)).

we are not aware of works that explored DP as a model for creating a diverse ensemble of experts for clustering tasks.


Exceptions include the hierarchical phrase-based SMT method of learning ITGs (Chiang, 2005), which does not rely on external resources.

unlike Chiang (2005) where huge numbers of (linguistically questionable) phrase translations are essentially memorized, our present work aims at inducing syntactic categories at an early stage in the learning, as occurs in child language acquisition.


The same modest improvement was observed when splitting the grammar with segment length 4, where we moved from 45.0 cross-entropy to 44.5 (model fstg_c_c to fstg_c_c_s2).

splitting again gave 42.9 (moving to fstg_c_c_s2_s2), and a third time gave 39.5 (moving to fstg_c_c_s2_s2_s2).


From the second column of the table we can observe that the correct translation is learned to be the best translation after bootstrapping with LTGs alone.

singleton translation and other synonyms of 里面 do not appear in the top five translations.


Our focus in this paper is hence restricted to distinguishing between codes TR2 and TR3.

our approach is extensible to the detection of TR1 if appropriate training data were available.


A2: Pronoun Count -Pronouns are typically discarded in most text classification applications in the pre-processing stage under the assumption that they occur too frequently to bear any information.

in (Campbell and Pennebaker, 2003) it was shown that changes in the way people use pronouns when writing about traumatic experiences is a powerful predictor of changes in physician visits or an indicator of their general health.


In all of these labels, there is extensive description of the distress condition in the messages.

there are many labels that are implied in the text, and are inconsistently inferred even amongst human annotators.


The higher the length of the vector, the longer the memory of the passage of an ant is kept.

the proportion of odour components deposited has the inverse effect.


We reimplemented them with the E x t Lesk measure and used the optimal parameters provided.

the similarity values are higher than with the standard Lesk algorithm and we had to adapt the parameters to reflect that difference.


When a voting strategy is used, ACA is ahead with 1.75% compared to Degree.

it is important to note that when looking a the scores per part of speech, Degree exhibits notably higher results for nouns (85% versus 76.35%), while ACA performs much better for adverbs adjectives and verbs (83.98%, 82.44%, 74.16%).


The vote strategy allowed ACA to reach the level of the first sense baseline, to beat the state-of-the-art unsupervised systems and the lowest performing supervised systems.


The estimation of the parameters, whether manual or through an automated learning algorithm prevent these algorithms from being entirely unsupervised.

the degree of supervision remains far below supervised approaches that use training corpora approximately 1000 times larger.


The two annotations can be deterministically derived from one another and express a similar syntactic relation, namely in both cases the PP is the complement of the adjective "sure".

selecting one of the alternatives (the preposition) and not the other (the NP) results in a more learnable scheme.


Predictability yields similar results to learnability in the infinitive verb structure as well, both showing no strong bias.

in the two other structures results diverge.


The fact that it correlates with learnability can provide a partial explanation to the learnability results.

it has several disadvantages compared to learnability.


These approaches rely on both a knowledge source such as WordNet (Miller et al., 1993) and a semantic distance metric.

in the current approach we do not need such a knowledge source or similarity judgments, and since our approach is data-driven, selectors function as an abstraction of word instance context rather than as a list of semantically similar words.


In sentence 2 the immediate context before and after the target word seem contradictory: "... action have occurred..." implies occur-v.1 (to "happen or take place"), while "... occurred to him ..." implies occur-v.1 (to "come to mind").

when considering the whole context occur-v.2 fits best, and in fact, the selectors for this instance match with many of the most frequent selectors for occur-v.2 such as 'belong', 'lead', 'listen', and 'try'.


More importantly, the new morphology brought by the those phenomenon complicates any processing based on regular unknown word identification through suffix analysis.

our general annotation strategy consists in staying as close as possible from the French Treebank guidelines (Abeillé et al., 2003) in order to have a data set as compatible, as much as possible, with existing resources.


Regarding the Google web bank, the way annotation guidelines had to be extended to deal with user generated content is largely consistent between both treebanks.

our treebank differs from the Google Web Treebank in several aspects.


Majority of the research focussed on machine learning (ML) approaches (Bikel et al., 1999;Borthwick, 1999;Sekine, 1998;Lafferty et al., 2001a;Yamada et al., 2001) because these are easily trainable, adaptable to different domains and languages as well as their maintenance are also being less expensive.

rule-based approaches lack the ability of dealing with the problems of robustness and portability.


Some works on NER for Indian languages can be found in (Ekbal and Saha, 2011;Bandyopadhyay, 2009b, 2007;Li and McCallum, 2004;Patel et al., 2009;Srikanth and Murthy, 2008;Shishtla et al., 2008;Vijayakrishna and Sobha, 2008).

the works related to NER in Indian languages are still in the nascent stages due to the potential facts such as (Ekbal and Saha, 2011): • Unlike English and most of the European languages, Indian languages lack capitalization information, which plays a very important role in NE identification.


For example, in 34, the suffix -t-is rejected as being a conditional mood marker as it belongs to the category of must-end markers and cannot be followed by any other verb morpheme (except for the gender-number marker).

the habitual -t-may be followed by a tense auxiliary.


Such a leader is a skilled task leader, which corresponds to the social science theory put forth in Beebe and Masterson (2006).

a thought leader in the group is someone who has credibility in the group and introduces ideas or thoughts that others pick up on or support.


1998;Stolcke, et al., 2000;Ji & Bilmes, 2006, inter alia) or to map them onto subsequences or "dialogue games" (Carlson 1983;Levin et al., 1998), from which participants' functional roles in conversation (though not social roles) may be extrapolated (e.g., Linell, 1990;Poesio and Mikheev, 1998;Field et al., 2008).

the effects of speech acts on social behaviors and roles of conversation participants have not been systematically studied.


We could choose another baseline, such as selecting the participant with the most number of turns as the Leader or Influencer.

we see similar performance for such baselines as the random one.


This research field is becoming increasingly important.

most previous work depend on the availability of external knowledge sources or assume a static context around terms and expect the names to be the only changing factor.


The results presented in this paper are "anecdotal" (to use the words of the authors) and thus cannot be used for direct comparison.

because of the promising results we use the same method for defining a context.


The latter is an easier case of evolution because of the overlapping first name and can be targeted using entity consolidation or linking techniques (Shen et al., 2012;Ioannou et al., 2010).

most existing techniques do not take historic changes into account and only focus on merging concurrent representations of the same entity.


By comparing the boxplots of the two PRESEMT versions for BLEU, it can be seen that boxplots for PRESEMT-1 occupy similar ranges of the score range to those of PRESEMT-2.

the range for PRESEMT-1 is displaced towards lower values of BLEU in comparison to PRESEMT-2, while also a larger number of outliers exist for PRESEMT-1.


Thus, most median values of PRESEMT-1 for different sentence sizes are placed at lower BLEU levels, below the 0.15 mark, with only a few outliers exceeding the limited range of the boxplots.

when turning to PRESEMT-2, the median values are higher, exceeding 0.200 in most cases and even reaching 0.400 in some of the cases.


Conventional solutions for this problem rely on the use of tf-idf and similar measures, which indicate whether a keyword is specifically important within a selected document set or whether it is also frequent within the corpus of all documents.

when looking at geolocated documents the situation is quite different.


Furthermore, in the case of Twitter messages, which are bound to 140 characters, there will rarely be more than one occurrence of a specific term and it is thus not meaningful to calculate the term frequency for a single message.

if a set of messages M is examined -e.g.


In this mode, at least some terms related to the event (panel, international, comic) achieve a high ranking and can be seen in the visualization.

the top ranking terms are still dominated by terms of regional prominence (san, diego, center).


A harder problem emerging with the availability of data in many languages is the problem of discriminating between closely related languages.

only a few researchers dealt with that problem in the past.


An additional remark should be made that this system focuses on domain robustness and not the problem of discriminating similar languages.

textCat only uses the most frequent N-grams per language and Lingua::Identify uses prefixes, suffixes and frequent words and, apparently, none of these features can well discriminate similar languages such as the ones we deal with.


Blacklists could be built manually using linguistic intuitions by native speakers.

it is also possible to derive such data sets from corpora simply by comparing word frequencies.


We could even introduce yet another threshold to define a margin that describes the grey area of uncertain decisions.

in that case, we would end up with a classifier that uses the following decision rule: we do not apply this method in the present paper as this introduces yet another free parameter that needs to be adjusted.


In that case, they would easily end up in blacklists without being appropriate for language discrimination.

certain punctuation differences may also work quite well for distinguishing between languages.


The obtained p-values are presented in Table 5  The difference between the Naive Bayes and Blacklist classifiers has shown not to be statistically significant on this size of the evaluation set (p = 0.188), but the difference is an interesting fact that should be looked into in future work.

the difference between the Naive Bayes and Blacklist classifiers to the other classifiers is highly statistically significant (p < 0.001) while the difference between our baseline Markov chain and the langid.py classifier (0.027) is marginal (p = 0.094).


As expected, we can see a significant decline of the accuracy with very short documents.

already at about 70 words we have an overall performance of over 90%.


The unsupervised setting is important in itself, and the development of these methods arguably provides interesting insights into modeling implicit supervision signals present in unlabeled data.

given that small amounts of labeled data are often easy to obtain, it is surprising that no previous work that we are aware of looked into integration of labeled data into unsupervised SRL systems.


In the labeling stage, semantic roles are represented by clusters of arguments, and labeling a particular argument corresponds to deciding on its role cluster.

instead of dealing with argument occurrences directly, in BayesSRL they are represented as predicate-specific syntactic signatures, called argument keys.


We assume that there exists a fixed latent mapping g from argument keys to semantic roles and any such mapping is a-priori equiprobable, P(g) = const.

when generating a label g(k) for a key k, we assume that it can be replaced by any of the remaining R − 1 roles with small probability γ.


Similarly to SRL, semisupervised approaches in this area are also typically based on bootstrapping techniques (e.g., (Agichtein and Gravano, 2000;Rosenfeld and Feldman, 2007)) and often achieve impressive results.

their set-up is arguably different from ours as relation extractors are generally more precision-oriented, focus primarily on binary relations and can partially sidestep the complexity of language.


Continuous discourse relations are claimed to be easier to process and more expected than other types.

relations that are discontinuous (for example adversatives) would be less expected and more difficult to process.


It would also suggest that the annotators of the corpus tended to mark that relation even in the absence of direct textual signals.

a small value of implicitness means that the discourse relation is expressed with an explicit discourse cue more often than average, and we would interpret that as the relation being not easily predictable or difficult to process, such that an explicit marker is needed to avoid a peak in information density.


In the third and fourth rules, we see the structure of the English phrase and how it is translated into French.

to the baseline model, this approach can handle both token and phrase translations.


Researchers have proposed several ways to cope with this situation, and we plan to integrate some of these in our future work.

an alternative approach is to exploit grammar rules directly: this allows us to increase variety without introducing noisy translations, and we discuss this approach next.


Once these are fixed, we can use a subset of the topics to appropriately tune parameters for the rest.

better tuning methods need to be devised for a truly robust approach to combining these CLIR models.


These 'inductive' approaches have achieved respectable accuracy (60-70 F-measure against a dictionary) and are more portable than earlier methods.

their ability to improve in accuracy is limited by their inability to incorporate information beyond the GR co-occurrences and heuristics that identify candidate SCFs on a per-sentence basis.


sentence), not every GR type will be instantiated.

to model the multi-way co-occurrences in a tensor framework, each instance must have a feature for every mode to be incorporated into the tensor.


In summary, the proposed criteria for extraction of translation candidates are not biased towards high-frequency or low-frequency words, as they treat all words the same, trying to find potential candidates according to the defined set of features.

in practice, the majority of the matched candidates will be low-frequency words.


For instance, we could opt for another strategy when deciding how to change the size of sub-corpora, skip already processed sub-corpora, remodel the criteria for extraction from Section 2.2, change stopping criteria, or employ a procedure for the sub-corpora sampling different from the one presented in Subsection 2.3.1.

our main goal is to propose a general framework for lexicon extraction when the data sampling approach is employed, where other researchers could design their own algorithms built upon the same idea.


If that is not true, we could use SampLEX only to extract source words for which a translation might be found, but the particular translation for each extracted word could then be obtained by some other method.

it is not the case, as the results in Tables 5 and 6 reveal.


Online discussion forums are a valuable means for users to resolve specific information needs, both interactively for the participants and statically for users who search/browse over historical thread data.

the complex structure of forum threads can make it difficult for users to extract relevant information.


From Table 4 we can see a similar trend to that in Section 5.1, with our method improving over both baselines when we use either gold-standard or automatically-predicted features.


We are also able to attain improvements in Solvedness classification accuracy using automatically-predicted thread discourse structure, although not at a level of statistical significance.

simulations suggest that as we improve the F-score of thread discourse structure parsing, the Solvedness classification accuracy will increase disproportionately.


Previous study indicates that the presence of discourse markers can great ly help relation recognition and the most general senses (i.e., co mparison, contingency, temporal and expansion ) can be disambiguated with 93% accuracy based solely on the discourse connectives (Pitler et al., 2008).

the absence of exp licit textual cues makes it very difficult to identify the implicit d iscourse relations.


The previous research on Chinese opinion analysis focuses on subjective expressions (opinionated sentences) (Liu, 2010), as in the Multilingual Opinion Analysis Task (MOAT) of NTCIR (Seki et al., 2010).

some objective expressions that describe positive or negative facts are also informative in that they express some kinds of evaluations.


how many arguments are shared by the two predicates (NUM_SHARED_ARGS)?

ψ S pays more attention to inferring the lexical semantic relations of edits.


The verbose, non-colloquial and monologue nature of much of Web text is not a good match for the characteristics of human-human dialogue.

there are some other sources on the Web that have more potential for this purpose, such as human-written comments on news websites (e.g., NYTimes.com (Marge et al., 2010)) and online forums (e.g., RottenTomatoes.com (Huang et al., 2007), 2ch.net (Inoue et al., 2011)).


Adding new patterns to support more types of questions or using punctuation to detect questions may seem to be straightforward solutions.

our intention to potentially incorporate speech input will render the latter option useless, while the former solution will increase false positives during question detection.


The POS feature stream contributes the most to the factored RNNLM.

rNNLM (Mikolov et al., 2011b) does not use it.


In both of the two examples (En 3 and En 4), supporting carries subjective senses.

the corresponding translations of supporting in Romanian for En 3 and En 4 are different: sprijinirea (in Ro 3) and sustinerea (in Ro 4).


It would be difficult to tell two domains apart based on the HMM labels since the same HMM states may generate many similar words from a variety of domains.

these unsupervised representations are not specifically discriminative for any NLP tasks.


Unsupervised discriminative model can directly learn synchronous grammars in a theoretically justified manner just like generative model.

the advantage over generative model is that it is able to easily incorporate word alignment information which has been proved useful in the two-step pipeline.


Z(s) is the partition function: Such a discriminative latent variable model is not new to SMT (Blunsom et al., 2008;Kääriäinen, 2009;Xiao et al., 2011).

we are distinguished from previous work by applying this model to synchronous grammar induction.


The purpose of the latent variable model in such previous work is to do max-translation decoding and training (Blunsom et al., 2008), or to eliminate the gap between heuristic extraction and decoding (Kääriäinen, 2009), instead of grammar induction as synchronous rules are still extracted by the heuristic two-step pipeline.

our interest lies in using latent variable model to learn synchronous grammar directly from sentence pairs.